{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verifying the Assumptions of Linear Regression in Python and R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear regression is one of the most basic machine learning algorithms and is often used as a benchmark for more advanced models. I assume the reader knows the basics of how linear regression works and what a regression problem is in general. That is why in this short article I would like to focus on the assumptions of the algorithm - what they are and how we can verify them using Python and R. I do not try to apply the solutions here, but indicate what they could be.\n",
    "\n",
    "In this article I mainly use Python (in Jupyter Notebook), but I also show how to use `rpy2` - an 'interface between both languages to benefit from the libraries of one language while working in the other'. It enables us to run both R and Python in the same Notebook and even transfer objects between the two. Intuitively, we need to have R installed on our computer as well. \n",
    "\n",
    "Disclaimer: Some of the cells using `rpy2` do not work and I have to 'cheat' by running them in R to show the results. This is mostly the case for cells using `ggplot2`. Nonetheless, I leave the code in these cell. Let me know in the comments if this works for you :)\n",
    "\n",
    "Let's start!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this article I use a classic regression dataset - Boston house prices. For simplicity, I only take the numeric variables. That's why I drop the only boolean feature - CHAS. I am not going to go deeper into the meaning of the features, but this can always be inspected by running `print(boston.DESCR)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T11:40:48.109064Z",
     "start_time": "2019-06-18T11:40:46.649791Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS    NOX     RM   AGE     DIS  RAD    TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31  0.538  6.575  65.2  4.0900  1.0  296.0     15.3   \n",
       "1  0.02731   0.0   7.07  0.469  6.421  78.9  4.9671  2.0  242.0     17.8   \n",
       "2  0.02729   0.0   7.07  0.469  7.185  61.1  4.9671  2.0  242.0     17.8   \n",
       "3  0.03237   0.0   2.18  0.458  6.998  45.8  6.0622  3.0  222.0     18.7   \n",
       "4  0.06905   0.0   2.18  0.458  7.147  54.2  6.0622  3.0  222.0     18.7   \n",
       "\n",
       "        B  LSTAT  \n",
       "0  396.90   4.98  \n",
       "1  396.90   9.14  \n",
       "2  392.83   4.03  \n",
       "3  394.63   2.94  \n",
       "4  396.90   5.33  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_boston\n",
    "\n",
    "# load data\n",
    "boston = load_boston()\n",
    "X = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "X.drop('CHAS', axis=1, inplace=True)\n",
    "y = pd.Series(boston.target, name='MEDV')\n",
    "\n",
    "# inspect data\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most readers would probably estimate a linear regression model like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T11:42:31.049558Z",
     "start_time": "2019-06-18T11:42:31.040860Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [-1.13139078e-01  4.70524578e-02  4.03114536e-02 -1.73669994e+01\n",
      "  3.85049169e+00  2.78375651e-03 -1.48537390e+00  3.28311011e-01\n",
      " -1.37558288e-02 -9.90958031e-01  9.74145094e-03 -5.34157620e-01]\n",
      "Intercept: 36.89195979693238\n",
      "R^2 score: 0.7355165089722999\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X, y)\n",
    "\n",
    "print(f'Coefficients: {lin_reg.coef_}')\n",
    "print(f'Intercept: {lin_reg.intercept_}')\n",
    "print(f'R^2 score: {lin_reg.score(X, y)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which is of course not a wrong approach. However, coming to Python from R, I had a bit higher expectations about the amount of information I receive by default. To run R inside our Notebook we first need to run the magic command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:21:35.991542Z",
     "start_time": "2019-06-03T19:21:35.762570Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afterwards, using another magic command indicate that the cell contains R code. At this step, I also use the input command `-i` to indicate that I am passing an object from Python to R. To retrieve the output from R to Python we can use `-o`. Running the two lines results in much more information, including statistical significance and some metrics like R^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:21:37.415408Z",
     "start_time": "2019-06-03T19:21:37.234986Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "lm(formula = y ~ ., data = cbind(X, y))\n",
      "\n",
      "Residuals:\n",
      "     Min       1Q   Median       3Q      Max \n",
      "-13.3968  -2.8103  -0.6455   1.9141  26.3755 \n",
      "\n",
      "Coefficients:\n",
      "              Estimate Std. Error t value Pr(>|t|)    \n",
      "(Intercept)  36.891960   5.146516   7.168 2.79e-12 ***\n",
      "CRIM         -0.113139   0.033113  -3.417 0.000686 ***\n",
      "ZN            0.047052   0.013847   3.398 0.000734 ***\n",
      "INDUS         0.040311   0.061707   0.653 0.513889    \n",
      "NOX         -17.366999   3.851224  -4.509 8.13e-06 ***\n",
      "RM            3.850492   0.421402   9.137  < 2e-16 ***\n",
      "AGE           0.002784   0.013309   0.209 0.834407    \n",
      "DIS          -1.485374   0.201187  -7.383 6.64e-13 ***\n",
      "RAD           0.328311   0.066542   4.934 1.10e-06 ***\n",
      "TAX          -0.013756   0.003766  -3.653 0.000287 ***\n",
      "PTRATIO      -0.990958   0.131399  -7.542 2.25e-13 ***\n",
      "B             0.009741   0.002706   3.600 0.000351 ***\n",
      "LSTAT        -0.534158   0.051072 -10.459  < 2e-16 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Residual standard error: 4.787 on 493 degrees of freedom\n",
      "Multiple R-squared:  0.7355,\tAdjusted R-squared:  0.7291 \n",
      "F-statistic: 114.3 on 12 and 493 DF,  p-value: < 2.2e-16\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i X -i y\n",
    "\n",
    "lin_reg <- lm(y ~ ., data = cbind(X, y))\n",
    "summary(lin_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course Python does not stay behind and we can obtain similar level of details using another popular library - `statsmodels`. One thing to bear in mind is that when using linear regression in `statsmodels` we need to add a column of ones to serve as intercept. For that I use `add_constant`. The results are much more informative than the default ones from `sklearn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T11:43:11.221663Z",
     "start_time": "2019-06-18T11:43:11.192014Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eryklewinson/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>MEDV</td>       <th>  R-squared:         </th> <td>   0.736</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.729</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   114.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 18 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>7.30e-134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:43:11</td>     <th>  Log-Likelihood:    </th> <td> -1503.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3034.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   493</td>      <th>  BIC:               </th> <td>   3088.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td>   36.8920</td> <td>    5.147</td> <td>    7.168</td> <td> 0.000</td> <td>   26.780</td> <td>   47.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CRIM</th>    <td>   -0.1131</td> <td>    0.033</td> <td>   -3.417</td> <td> 0.001</td> <td>   -0.178</td> <td>   -0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ZN</th>      <td>    0.0471</td> <td>    0.014</td> <td>    3.398</td> <td> 0.001</td> <td>    0.020</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INDUS</th>   <td>    0.0403</td> <td>    0.062</td> <td>    0.653</td> <td> 0.514</td> <td>   -0.081</td> <td>    0.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NOX</th>     <td>  -17.3670</td> <td>    3.851</td> <td>   -4.509</td> <td> 0.000</td> <td>  -24.934</td> <td>   -9.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RM</th>      <td>    3.8505</td> <td>    0.421</td> <td>    9.137</td> <td> 0.000</td> <td>    3.023</td> <td>    4.678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AGE</th>     <td>    0.0028</td> <td>    0.013</td> <td>    0.209</td> <td> 0.834</td> <td>   -0.023</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DIS</th>     <td>   -1.4854</td> <td>    0.201</td> <td>   -7.383</td> <td> 0.000</td> <td>   -1.881</td> <td>   -1.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RAD</th>     <td>    0.3283</td> <td>    0.067</td> <td>    4.934</td> <td> 0.000</td> <td>    0.198</td> <td>    0.459</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TAX</th>     <td>   -0.0138</td> <td>    0.004</td> <td>   -3.653</td> <td> 0.000</td> <td>   -0.021</td> <td>   -0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PTRATIO</th> <td>   -0.9910</td> <td>    0.131</td> <td>   -7.542</td> <td> 0.000</td> <td>   -1.249</td> <td>   -0.733</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>B</th>       <td>    0.0097</td> <td>    0.003</td> <td>    3.600</td> <td> 0.000</td> <td>    0.004</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LSTAT</th>   <td>   -0.5342</td> <td>    0.051</td> <td>  -10.459</td> <td> 0.000</td> <td>   -0.635</td> <td>   -0.434</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>190.856</td> <th>  Durbin-Watson:     </th> <td>   1.016</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 898.352</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.619</td>  <th>  Prob(JB):          </th> <td>8.42e-196</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.668</td>  <th>  Cond. No.          </th> <td>1.51e+04</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.51e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   MEDV   R-squared:                       0.736\n",
       "Model:                            OLS   Adj. R-squared:                  0.729\n",
       "Method:                 Least Squares   F-statistic:                     114.3\n",
       "Date:                Tue, 18 Jun 2019   Prob (F-statistic):          7.30e-134\n",
       "Time:                        13:43:11   Log-Likelihood:                -1503.8\n",
       "No. Observations:                 506   AIC:                             3034.\n",
       "Df Residuals:                     493   BIC:                             3088.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         36.8920      5.147      7.168      0.000      26.780      47.004\n",
       "CRIM          -0.1131      0.033     -3.417      0.001      -0.178      -0.048\n",
       "ZN             0.0471      0.014      3.398      0.001       0.020       0.074\n",
       "INDUS          0.0403      0.062      0.653      0.514      -0.081       0.162\n",
       "NOX          -17.3670      3.851     -4.509      0.000     -24.934      -9.800\n",
       "RM             3.8505      0.421      9.137      0.000       3.023       4.678\n",
       "AGE            0.0028      0.013      0.209      0.834      -0.023       0.029\n",
       "DIS           -1.4854      0.201     -7.383      0.000      -1.881      -1.090\n",
       "RAD            0.3283      0.067      4.934      0.000       0.198       0.459\n",
       "TAX           -0.0138      0.004     -3.653      0.000      -0.021      -0.006\n",
       "PTRATIO       -0.9910      0.131     -7.542      0.000      -1.249      -0.733\n",
       "B              0.0097      0.003      3.600      0.000       0.004       0.015\n",
       "LSTAT         -0.5342      0.051    -10.459      0.000      -0.635      -0.434\n",
       "==============================================================================\n",
       "Omnibus:                      190.856   Durbin-Watson:                   1.016\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              898.352\n",
       "Skew:                           1.619   Prob(JB):                    8.42e-196\n",
       "Kurtosis:                       8.668   Cond. No.                     1.51e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.51e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "X_constant = sm.add_constant(X)\n",
    "lin_reg = sm.OLS(y,X_constant).fit()\n",
    "lin_reg.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we see how to run linear regression in R and Python. Let's continue to the assumptions. I break these down into two parts:\n",
    "* assumptions from the Gauss-Markov Theorem\n",
    "* rest of assumptions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gauss-Markov Theorem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During your statistics or econometrics courses you might have heard the acronym BLUE in the context of linear regression. What was the meaning of it? According to the [Gauss–Markov theorem](https://en.wikipedia.org/wiki/Gauss%E2%80%93Markov_theorem), in a linear regression model the ordinary least squares (OLS) estimator gives the best linear unbiased estimator (BLUE) of the coefficients, provided that:\n",
    "* the expectation of errors (residuals) is 0\n",
    "* the errors are uncorrelated\n",
    "* the errors have equal variance - homoscedasticity of errors\n",
    "\n",
    "Also, 'best' in BLUE means resulting in lowest variance of the estimate, in comparison to other unbiased, linear estimators. \n",
    "\n",
    "For the the estimator to be BLUE, the residuals do not need to follow normal (Gaussian) distribution, nor do they need to be [independent and identically distributed](https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linearity of the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dependent variable (y) is assumed to be a linear function of the independent variables (X, features) specified in the model. The specification must be linear in its parameters. Fitting a linear model to data with non-linear patterns results in serious prediction errors, especially out-of-sample (data not used for training the model)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To detect nonlinearity one can inspect plots of observed vs. predicted values or residuals vs. predicted values. The desired outcome is that points are symmetrically distributed around a diagonal line in the former plot or around horizontal line in the latter one. In both cases with a roughly constant variance. \n",
    "\n",
    "Observing a \"bowed\" pattern indicates that the model makes systematic errors whenever it is making unusually large or small predictions. When the model contains many features, nonlinearity can also be revealed by systematic patterns in plots of the residuals vs. individual features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-18T11:43:20.315317Z",
     "start_time": "2019-06-18T11:43:19.664237Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 551,
       "width": 884
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format ='retina'\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.stats.api as sms\n",
    "sns.set_style('darkgrid')\n",
    "sns.mpl.rcParams['figure.figsize'] = (15.0, 9.0)\n",
    "\n",
    "def linearity_test(model, y):\n",
    "    '''\n",
    "    Function for visually inspecting the assumption of linearity in a linear regression model.\n",
    "    It plots observed vs. predicted values and residuals vs. predicted values.\n",
    "    \n",
    "    Args:\n",
    "    * model - fitted OLS model from statsmodels\n",
    "    * y - observed values\n",
    "    '''\n",
    "    fitted_vals = model.predict()\n",
    "    resids = model.resid\n",
    "\n",
    "    fig, ax = plt.subplots(1,2)\n",
    "    \n",
    "    sns.regplot(x=fitted_vals, y=y, lowess=True, ax=ax[0], line_kws={'color': 'red'})\n",
    "    ax[0].set_title('Observed vs. Predicted Values', fontsize=16)\n",
    "    ax[0].set(xlabel='Predicted', ylabel='Observed')\n",
    "\n",
    "    sns.regplot(x=fitted_vals, y=resids, lowess=True, ax=ax[1], line_kws={'color': 'red'})\n",
    "    ax[1].set_title('Residuals vs. Predicted Values', fontsize=16)\n",
    "    ax[1].set(xlabel='Predicted', ylabel='Residuals')\n",
    "    \n",
    "linearity_test(lin_reg, y)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:19:31.267868Z",
     "start_time": "2019-06-03T19:19:31.252241Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R -i y\n",
    "\n",
    "p1 <- ggplot(lin_reg, aes(.fitted, .resid)) + geom_point()\n",
    "p1 <- p1 + stat_smooth(method=\"loess\") + geom_hline(yintercept=0, col=\"red\", linetype=\"dashed\")\n",
    "p1 <- p1 + xlab(\"Predicted\") + ylab(\"Residuals\")\n",
    "p1 <- p1 + ggtitle(\"Residuals vs. Predicted Values\") + theme_bw()\n",
    "\n",
    "df_plt <- data.frame(\"fitted\" = fitted(lin_reg), \"observed\" = X$medv)\n",
    "p2 <- ggplot(df_plt, aes(x=fitted, y=observed)) + geom_point()\n",
    "p2 <- p2 + stat_smooth(method=\"loess\") + geom_abline(intercept = 1, col=\"red\", linetype=\"dashed\")\n",
    "p2 <- p2 + xlab(\"Predicted\") + ylab(\"Observed\")\n",
    "p2 <- p2 + ggtitle(\"Observed vs. Predicted Values\") + theme_bw()\n",
    "\n",
    "grid.arrange(p2, p1, ncol=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspection of the plots shows that the linearity assumption is not satisfied. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Potential solutions:\n",
    "* non-linear transformations to dependent/independent variables\n",
    "* adding extra features which are a transformation of the already used ones (for example squared version)\n",
    "* adding features that were not considered before"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expectation (mean) of residuals is zero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one is easy to check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:21:43.668645Z",
     "start_time": "2019-06-03T19:21:43.664836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0012544153465325e-13"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.resid.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:21:43.952947Z",
     "start_time": "2019-06-03T19:21:43.943160Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 2.018759e-17\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "mean(lin_reg$resid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-02T22:27:22.197033Z",
     "start_time": "2019-06-02T22:27:22.193319Z"
    }
   },
   "source": [
    "The results are a bit different, as far as I know this is a numeric approximation issue. However, we can assume that the expectation of residuals is indeed 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No (perfect) multicollinearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In other words, the features should be linearly independent. What does that mean in practice? We should not be able use a linear model to accurately predict one feature using another one. Let's take X1 and X2 as examples of features. It could happen that X1 = 2 + 3 * X2, which violates the assumption.\n",
    "\n",
    "One scenario to watch out for is the 'dummy variable trap' - when we use dummy variables to encode a categorical feature and do not omit the baseline level from the model. This results in perfect correlation between the dummy variables and the constant term.\n",
    "\n",
    "Multicollinearity can be present in the model, as long as it is not 'perfect'. In the former case, the estimates are less efficient, but still unbiased. The estimates will be less precise and highly sensitive to particular sets of data.\n",
    "\n",
    "We can detect multicollinearity using [variance inflation factor](https://en.wikipedia.org/wiki/Variance_inflation_factor) (VIF). Without going into too much details, the interpretation of VIF is as follows: the square root of a given variable's VIF shows how much larger the standard error is, compared with what it would be if that predictor were uncorrelated with the other features in the model. If no features are correlated, then all values for VIF will be 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:21:45.976943Z",
     "start_time": "2019-06-03T19:21:45.954573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vif</th>\n",
       "      <td>1.787705</td>\n",
       "      <td>2.298257</td>\n",
       "      <td>3.949246</td>\n",
       "      <td>4.388775</td>\n",
       "      <td>1.931865</td>\n",
       "      <td>3.092832</td>\n",
       "      <td>3.954961</td>\n",
       "      <td>7.397844</td>\n",
       "      <td>8.876233</td>\n",
       "      <td>1.783302</td>\n",
       "      <td>1.344971</td>\n",
       "      <td>2.931101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CRIM        ZN     INDUS       NOX        RM       AGE       DIS  \\\n",
       "vif  1.787705  2.298257  3.949246  4.388775  1.931865  3.092832  3.954961   \n",
       "\n",
       "          RAD       TAX   PTRATIO         B     LSTAT  \n",
       "vif  7.397844  8.876233  1.783302  1.344971  2.931101  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "vif = [variance_inflation_factor(X_constant.values, i) for i in range(X_constant.shape[1])]\n",
    "pd.DataFrame({'vif': vif[1:]}, index=X.columns).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T23:56:45.076888Z",
     "start_time": "2019-05-31T23:56:45.073248Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "library(car)\n",
    "vif(lin_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To deal with multicollinearity we should iteratively remove features with high values of VIF. A rule of thumb for removal could be VIF larger than 10 (5 is also common). Another possible solution is to use PCA to reduce features to a smaller set of uncorrelated components."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-02T22:29:51.035860Z",
     "start_time": "2019-06-02T22:29:51.032279Z"
    }
   },
   "source": [
    "Tip: we can also look at correlation matrix of features to identify dependencies between them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Homoscedasticity (equal variance) of residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When residuals do not have constant variance (they exhibit heteroscedasticity), it is difficult to determine the true standard deviation of the forecast errors, usually resulting in confidence intervals that are too wide/narrow. For example, if the variance of the residuals is increasing over time, confidence intervals for out-of-sample predictions will be unrealistically narrow. Another effect of heteroscedasticity might also be putting too much weight to a subset of data when estimating coefficients - the subset in which the error variance was largest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate if the residuals are homoscedastic, we can look at a plot of residuals (or standardized residuals) vs. predicted (fitted) values. What should alarm us is the case when the residuals grow either as a function of predicted value or time (in case of time series). \n",
    "\n",
    "We can also use two statistical tests: Breusch-Pagan and Goldfeld-Quandt. In both of them the null hypothesis assumes homoscedasticity and a p-value below a certain level (like 0.05) indicates we should reject the null in favor of heteroscedasticity. \n",
    "\n",
    "In the snippets below I plot residuals (and standardized ones) vs. fitted values and carry out the two mentioned tests. To identify homoscedasticity in the plots, the placement of the points should be random and no pattern (increase/decrease in values of residuals) should be visible - the red line in the R plots should be flat. We can see that this is not the case for our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:20:57.196422Z",
     "start_time": "2019-06-03T19:20:56.427742Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Breusch-Pagan test ----\n",
      "                                      value\n",
      "Lagrange multiplier statistic  6.028613e+01\n",
      "p-value                        2.001794e-08\n",
      "f-value                        5.556828e+00\n",
      "f p-value                      5.935449e-09\n",
      "\n",
      " Goldfeld-Quandt test ----\n",
      "                    value\n",
      "F statistic  2.620956e+00\n",
      "p-value      1.251137e-13\n",
      "\n",
      " Residuals plots ----\n"
     ]
    },
    {
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kQCi7ZMkS6xqrW7euvvjiC0VERFj7e/Xqpblz52rEiBFWeAgAAAB4gyZNmujhhx+2RtWdPn1aU6dO1dSpUxUQEKDo6Gi1aNFCrVq1UnR09DlvTly2bJkV1ISHh2vy5Mlq2LChtT8mJkZ33XWX7r33XmVmZmrKlCm65557dMkll0iSRo8ebQV6gwcP9jhK7NFHH1W/fv0UGxsrwzD0zTffnFeoVxY1dUX48ssvrUCvTp06mjRpUoH39vbbb9ejjz6qwYMHa8OGDTp69KhGjhypDz/8sMB5pkyZYgV6l112mT777DMrcJOku+66S7fddpsef/xxud1ujR49Wt27d7feo7feesuaHebf//63x+UvBgwYoLvuuksHDhxQVlaWFi9ebAWDJanpziU7O1tPP/20Fej985//1DPPPFNgKY2+fftq8eLFGjZsmJxOp95++221bNlSTZo08XjOffv2qWHDhpo8eXKB96Nnz55q1aqVRo0aJUmaPn26hg4dWuplOwDgQuFfJQAoI+PHj1ejRo08/te0aVO1adNG9913nyZPnmwFel27dtXo0aMLhUoZGRlWsRUeHq6xY8cWKD7yBAcH67333lNAQIAkaeLEiXK73db+/fv3W49vuukmj/2+++67FRUVpSZNmqhWrVolfr179+61Ao7LLrtML730UqHRSVWrVtW7775b4IvyhZJ/OsRhw4YVOa1mr169VLNmTUm5U3XkhZ4lkfd+NmjQoFCgJ+XeQTl48GBFRETommuuKfBZlISPj481ajM9PV0rV6702O7bb7+1Ht95553W4+TkZGua1ejo6EKBnpQ7uqx///6qV6+e2rZtq/T09PPq49ni4+OLvO7P/m/gwIF/6rlK66uvvlJSUpKk3GujqHUnbrvtNt17772SpGPHjmnevHkF9uefdvWdd94pEOjl6d69u3UOAAAAwJs899xzevLJJ+Xn51dge2ZmptauXatx48bpgQceUOvWrTVo0CAtWbJEhmF4PFf+78Zvv/12gdApT1RUlHVjqWEY1mwjTqfTujEvPDy8yOUaHA6H+vfvb/2dv749l7KqqctbTk6OPvnkE0mSzWbT2LFjPb63QUFBGj9+vFVrL1u2rEB4ZpqmJk2aJCm3Th03bpzHurx9+/bWdJUZGRlaunSppNzlJfICwWbNmhW5nn1gYGCBaVHP5zMqiZkzZ+r48eOScm+IfvbZZz2GbJ06ddKTTz4pKXek44QJE4o975tvvunx/ejTp4+1REVKSooOHTr0Z18CAJQ5Qj0AKGd16tRRz549NW3aNL3//vvy9/cv1GbNmjVKS0uTlDs1Z16B4Ul4eLhuvPFGSdLvv/+uXbt2WfvyhzqTJ0+W0+n0eI65c+dq9uzZevPNN0v8OvKmsZRyv/gWNV1LYGCg+vTpU+LzltYLL7yg+fPn6+OPPy522kpJBUZyZWVllfg58t7PX3/9tcjRh61bt9by5cs1ZcqUAoFbScXExFiPz163LU/eegh56/DlqV69uvU5rF27Vtu2bSvyOZYuXarJkyef18jMv6q899HX11c9e/Ystm3+azXv7lhJSkxM1I4dOyTl3nFa3OjGRx55pMKn7AEAAADOZrPZ9Pjjj2v+/Pm6//77rZsdz5aVlaVly5ZpyJAhiomJ0c6dOwvsP3HihFVrXHnllbr22muLfM4ePXpo8ODBevfdd61ZTBwOh5YtW6YZM2Zo3LhxxU79WbduXetx3s2xJVFWNXV527p1q06ePClJuu6669SsWbMi2wYFBem+++6z/v7++++tx3FxcdZa4u3bt9ell15a5HkeeOABPfnkkxo9erTatGkjKfc9WbVqlb766iu9/vrrxfa5tJ9RSeSvyQYMGFBs23vvvVfBwcGSpNWrVxdZ60dERBT5vtpsNl1xxRXW33k3zQKAN2H6TQAoI7fddpu6du1q/e12u5WSkqLvv/9e69evl5S7OPmrr75qfVEuSt4dcVLuiK380wt6kr8I2rZtm5o2bWr16eOPP5ZhGJo3b57Wrl2rm266Sddff72uueYaVa9e/bxfZ54tW7ZYj/OmQSzK+UyvUVr+/v667LLLdNlll3ncn5SUpF9//VU//fSTDh48aG0v6s5TT7p27apJkybJ7Xbrscce01VXXaX27durbdu2atq0aZmso9aoUSNFRUVp165dWrlypdLT0wuMOty+fbsOHDggSerSpUuB6TkdDoduueUWLVq0SBkZGerdu7datWpl9bFRo0ZlHjaFhoaWeM3Eon40uJBOnTqlvXv3SpICAgK0YcOGYtubpik/Pz+5XC5rilMpt7g2TVPSua/3f/zjH6pXrx53dQIAAMArXXLJJRo5cqReeOEF7dq1Sxs2bNCmTZv0888/W0FYnt27d+vee+/VxIkT1apVK0m5NWdJvxuHhYV5nFqzRo0aRa6p5na7dejQIe3cuVOrVq0qsL2kyqqmLm9bt261Hp/rdwMpd/mB9957r9Cx+R/nfW5FadCgQaHRkjabTeHh4UXOuuNyuXTgwAFt375dixcvtraX9SjHvNcREBCg5s2bF9u2atWqatGihVasWCGXy6WdO3d6vD6L+s0gT/4RnXnTjwKANyHUA4AyUr9+fXXs2LHQ9j59+mjatGl65ZVXdPjwYT388MN68cUXi52iLyEhwXr89ddf6+uvvy5xP/KmGZRyv6yOGDFC//73v2UYhhITEzV9+nRNnz5dPj4+atq0qW644QZ17txZDRo0KPFzSLLuHpRyRx8WJ2/dhPJgGIZiY2O1adMm7du3T4cPH9bRo0cLFad58orRkhgyZIji4uK0adMmSbl3P8bFxVlTuVx33XW66aabdPPNN3uc2qWkYmJitGvXLp05c0ZLliwpMNVJ/qk384/qy/PSSy/pt99+0969e2UYhjZu3KiNGzdKyi2c27Ztq5tvvlkdOnRQ1apVS93HPP7+/h6ve2+RkJBgfcanTp3SoEGDSnxsamqqDMOQ3W637nKVckO7c7n44osJ9QAAAODVbDabte5Zv379ZBiGfv31V61atUqzZ8/WkSNHJOWO3Bs+fLgWL14sX1/fArXg2etZn69Tp05p+fLl2rFjhw4dOqTDhw8rPj7e4xrV51O7lUVNnZWVpbVr1xbb9tJLLz3vWro4+d9bT0s+nK1evXrW4/y/BeR/7GnZgPORmJioFStWaNeuXTp8+LAOHz6sY8eOeQy8zuczOpf09HRrtF1kZGSJ1rbL/37kfy/zO1etnj/gPZ+bgAGgvBDqAUA5uOeee5ScnKyxY8fKNE29/vrrqlGjhjp37uyx/enTp0v9XGevkXb//ffrqquu0qRJk7Rq1SprOgy3262tW7dq69atGjt2rNq3b69XX33Vmj/+XE6dOmU99jSFaH5FrW9X1jZv3qwXX3yxyHn8Q0JCdN1112nHjh06fPjweZ+/atWq+uKLLzRz5kzNmDGjwDQ0p0+f1uLFi7V48WL5+/vrscce04ABA0o1Mq5bt25655135HK5tGDBAivUc7vdWrRokaTcAq9FixaFjg0NDdWcOXM0ZcoUzZkzp8B7kZSUpG+//VbffvutQkJCNGzYMGv9hMrqz/y/ZJqmMjIyFBQUVOB6L0kYWl7XPAAAAFBW7Ha7oqKiFBUVpf79+2vSpEkaPXq0TNPU0aNHtWzZMnXu3LnAlISlvVHQNE199NFH+vTTT4ucJrFBgwaKjo7WrFmzzvv8ZVFTJyUlnfOmwMGDB3scifhnn1s6d519dpvMzEzrcVl8Rk6nU2+//bamTZvmMcCz2+268sordemll1pLRJSljIwM63Fx06fmV9T7kV9x070CwF8B/4oBQDkZOHCgNm3apPXr18swDL3wwgtq0qSJxzsb83/pnjlzZrHz6JdEs2bNNHbsWGVlZWnjxo1at26dNmzYoD179lh30q1atUoPPvigvvnmmxJ96c+bq17K/bLscDiKbFvUWn7n68yZM0Xui42N1SOPPGLd0Vm7dm21bt1al112merXr6/LL7/cumuvb9++pQr1JMnHx0d9+vRRnz59dOzYMa1Zs0YbNmzQhg0bCtzR+cEHHyg7O9tarPt8hIaG6vrrr9fy5cu1fv16nTx5UmFhYVq/fr01Yqy49fqqVKmi/v37q3///tq/f7/Wrl2r9evXa9OmTVY4lZqaqpEjR0pSpQ728hd1N9xwgyZOnFiq8+S/3kuyDmNZXfMAAADAn7Vt2zZt3rxZSUlJuvXWW3XVVVed8xhfX1899thjOnTokObMmSNJ+uWXX9S5c+cC9WJp11B7+eWXNX36dEm5IwabNWumZs2aqUGDBqpfv76uuOIKBQcH6/Dhw6UK9cq6pi4v+cOrktQd+YOv/LXPn/2MTNPUwIEDtWbNGkm510OLFi3UpEkT1a9fXw0aNFCjRo1UrVo1rV+//oKEevnfi6ICurMV9X4AQGVCqAcA5cRms+nNN99U165dlZGRofT0dA0fPlxTp04tNJor/9pj+/fvL7MCxN/fXx06dFCHDh0k5S5wPn/+fI0ZM0bZ2dk6ePCgvvnmG/Xu3fuc56pVq5b1+MiRIwoJCSmybf6pT86WfwqNc81Xn3+01NlGjRplBXqPP/64hgwZUuQad8Wd53zUqVNHvXr1Uq9evWSaprZv367PPvvMWqB88uTJ6tevX6mm4uzevbuWL18ut9utJUuWqG/fvlahZLPZig318qtfv77q16+v+++/X263W5s2bdJHH31krS03ZsyYSh3qhYWFWY/37dtX6vOcfb2fS3HXPAAAAFCeVq9erXHjxknKrSVKEurl6dChgxXqpaSkSCr4Hfv3338/5zliY2N10UUXKSIiQgEBAYqLi7MCveDgYE2cOLHIPhW1jMK5lEVNHRkZqd27d5fq+Usrf79LMp1//jb5Z90538/oxx9/1D/+8Q9FRESoSpUqWrRokRXoRURE6LPPPtOll17q8djSfkbnEhQUJH9/f2VlZeno0aPW0gjFKer9AIDK5Ny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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10730efd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 551,
       "width": 890
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format ='retina'\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.stats.api as sms\n",
    "sns.set_style('darkgrid')\n",
    "sns.mpl.rcParams['figure.figsize'] = (15.0, 9.0)\n",
    "\n",
    "def homoscedasticity_test(model):\n",
    "    '''\n",
    "    Function for testing the homoscedasticity of residuals in a linear regression model.\n",
    "    It plots residuals and standardized residuals vs. fitted values and runs Breusch-Pagan and Goldfeld-Quandt tests.\n",
    "    \n",
    "    Args:\n",
    "    * model - fitted OLS model from statsmodels\n",
    "    '''\n",
    "    fitted_vals = model.predict()\n",
    "    resids = model.resid\n",
    "    resids_standardized = model.get_influence().resid_studentized_internal\n",
    "\n",
    "    fig, ax = plt.subplots(1,2)\n",
    "\n",
    "    sns.regplot(x=fitted_vals, y=resids, lowess=True, ax=ax[0], line_kws={'color': 'red'})\n",
    "    ax[0].set_title('Residuals vs Fitted', fontsize=16)\n",
    "    ax[0].set(xlabel='Fitted Values', ylabel='Residuals')\n",
    "\n",
    "    sns.regplot(x=fitted_vals, y=np.sqrt(np.abs(resids_standardized)), lowess=True, ax=ax[1], line_kws={'color': 'red'})\n",
    "    ax[1].set_title('Scale-Location', fontsize=16)\n",
    "    ax[1].set(xlabel='Fitted Values', ylabel='sqrt(abs(Residuals))')\n",
    "\n",
    "    bp_test = pd.DataFrame(sms.het_breuschpagan(resids, model.model.exog), \n",
    "                           columns=['value'],\n",
    "                           index=['Lagrange multiplier statistic', 'p-value', 'f-value', 'f p-value'])\n",
    "\n",
    "    gq_test = pd.DataFrame(sms.het_goldfeldquandt(resids, model.model.exog)[:-1],\n",
    "                           columns=['value'],\n",
    "                           index=['F statistic', 'p-value'])\n",
    "\n",
    "    print('\\n Breusch-Pagan test ----')\n",
    "    print(bp_test)\n",
    "    print('\\n Goldfeld-Quandt test ----')\n",
    "    print(gq_test)\n",
    "    print('\\n Residuals plots ----')\n",
    "\n",
    "homoscedasticity_test(lin_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T23:56:45.083547Z",
     "start_time": "2019-05-31T23:56:45.079263Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "library(lmtest)\n",
    "\n",
    "par(mfrow=c(2,2))  # set 2 rows and 2 column plot layout\n",
    "plot(lin_reg)\n",
    "\n",
    "# Breusch-Pagan test\n",
    "print(bptest(lin_reg, data = X, studentize = TRUE))\n",
    "# Goldfeld-Quandt\n",
    "print(gqtest(lin_reg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results indicate that the assumption is not satisfied and we should reject the hypothesis of homoscedasticity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Potential solutions:\n",
    "* log transformation of dependent variable\n",
    "* in case of time series, deflating a series if it concerns monetary value\n",
    "* using ARCH (auto-regressive conditional heteroscedasticity) models to model the error variance. An example might be stock market, where data can exhibit periods of increased or decreased volatility over time (volatility clustering, see [this article](https://towardsdatascience.com/introduction-to-quantitative-finance-part-i-stylised-facts-of-asset-returns-5190581e40ea) for more information)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No autocorrelation of residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This assumption is especially dangerous in time-series models, where serial correlation in the residuals implies that there is room for improvement in the model. Extreme serial correlation is often a sign of a badly mis-specified model. Another reasons for serial correlation in the residuals could be violation of the linearity assumption or due to bias that is explainable by omitted variables (interaction terms or dummy variables for identifiable conditions). An example of the former case might be fitting a (straight) line to data, which exhibits exponential growth over time.\n",
    "\n",
    "This assumption also has meaning in case of non-time-series models. If residuals always have the same sign under particular conditions, it means that the model systematically underpredicts/overpredicts what happens when the predictors have a particular configuration. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate if autocorrelation is present, I use ACF (autocorrelation function) plots and Durbin-Watson test.\n",
    "\n",
    "In the former case, we want to see if the value of ACF is significant for any lag (in case of no time-series data, row number is used). While calling the function, we indicate the significance level (see (this article)[https://towardsdatascience.com/introduction-to-power-analysis-in-python-e7b748dfa26] for more details) we are interested in and the critical area is plotted on the graph. Significant correlations lie outside of that area. \n",
    "\n",
    "Note: when dealing with data without the time dimension, we can alternatively plot the residuals vs. the row number. In such cases rows should be sorted in a way that (only) depends on the values of the feature(s).\n",
    "\n",
    "The second approach is using the Durbin-Watson test. I do not go into details how it is constructed, but provide high level overview. The test statistic provides a test for significant residual autocorrelation at lag 1. The DW statistic is approximately equal to `2(1-a)`, where `a` is the lag-1 residual autocorrelation. The DW test statistic is located in the default summary output of `statsmodels`'s regression.\n",
    "\n",
    "Some notes on the Durbin-Watson test:\n",
    "* the test statistic always has value between 0 and 4\n",
    "* value of 2 means that there is no autocorrelation in the sample\n",
    "* values `<2` indicate positive autocorrelation, values `>2` negative one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:21:06.490241Z",
     "start_time": "2019-06-03T19:21:06.148401Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eryklewinson/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
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1QqAGAABraH9vd3Z0lA/KdnS05oo93WtUEQAAAHAqAjUAAFhDXW0tueaiXent6VrQ/rGQpLenK9devCudW1pqUR4AAACwiE21LgAAADaarraWHNh7bgbHJnLg6l9N85atmT46kk/ecL02jwAAAFCHBGoAAFAj29tbM9L353O+/ngNqwEAAACWIlADqEODoxO5d2A445PTaWtpzgU7O61YAAAAAACoEYEaQB0ZGp/MzX0DOdg/lNKc7bf2DWR3T1f293anq809dQAAAAAA1lJTrQsAYMbQ+GSuu/3B9J0UpiVJKUlf/1Cuu/3BDB+drEV5AAAAAAAblhVqAHXi5r6BHB6dKDvm8OhEvnDPQA7sPXeNqgKojpa1AAAAVMprSNYTgRpAHRgcncjB/qGKxh7sH8rg2IQfLoC6omUtAAAAlfIakvVIoAZQB+4dGF7Q5nEppST3PTqcfc/ZsZolAVRstmXtYqtsZ1vWHjoynmsv3pXOLV4QAfXJu6MBANaG15CsVwI1gDowPjld1fixKscDrCYta4H1zLujAQDWlteQrFdNtS4AgKStpbmq8e1VjgdYLafTshagXsy+O7rvpDAteerd0dfd/mCGj07WojwAgIbjNSTrmUANoA5c0N2ZQoVjC0nO39m5muUAVOx0WtbWwuDoRL76/cdy28GBfP1Hh70oA5JU9+7oWhkcncjXf3Q4X/n+Y+YvAGDdWy+vIWExWj4C1IHtHa3Z3dOVvgreobO7p8v9PIC6Ue8ta7VyA5ZyOu+OXsufwcxfAEAjqvfXkFCOFWoAdWJ/b3d2dJT/Jc2OjtZcsad7jSoCOLV6blmrlRtQTj2/O9r8BQA0qnp+DQmnIlADqBNdbS255qJd6e3pWtD+sZCkt6cr1168K51bvBMZqB/13LJ2PbRyA2qnnt8dbf4CABpVPb+GhFPR8hGgjnS1teTA3nMzODaRA1f/apq3bM300ZF88obrtXkE6lK9tqyt91ZuQO3V67ujzV8AQCOr19eQUAkr1ADq0Pb21oz0/XmGvn1LRvr+3A8PQF2rx5a19dzKDagP9fruaPMXANDo6vE1JFRCoAYAwLLUY8vaem7lBtSH2XdHV2It3x1t/gIAGl09voaESmj5CADAstVby9p6beUG1Jf9vd05dGS87P3K1vrd0eYvAGAjqLfXkFAJK9QAAFgx9dKytl5buQH1pR7fHW3+AgA2knp5DQmVsEINAICG40bXQKXq7d3R5i8AAKhPAjUAABpSPbZyA+rX7Lujn/r64zWrxfwFAAD1R8tHAAAaUj22cgOohPkLAADqjxVqAAA0rHpr5QZQKfMXAADUF4EaAAANr55auQFUw/wFAAD1QctHAAAAAAAAKMMKNQAAYJ7B0YncOzCc8cnptLU054KdnVrMAQAAsKEJ1AAAgCTJ0Phkbu4byMH+oZTmbL+1byC7e7qyv7c7XW0tNasPAAAAakXLRwAAIEPjk7nu9gfTd1KYliSlJH39Q7nu9gczfHSyFuUBAABATQnUAACA3Nw3kMOjE2XHHB6dyBfuGVijioB6Njg6ka//6HC+8v3H8vUfHc7gWPn5AwAA1jstHwEAYIMbHJ3Iwf6hisYe7B/K4NiEe6rBBqU1LAAAG5UVagAAsMHdOzC8oM3jUkpJ7nt0eDXLAeqU1rAAAGxkAjUAANjgxienqxo/VuV4oDFoDQsAwEYmUAMAgA2uraW5qvHtVY4H1r/TaQ0LAACNRKAGAAAb3AXdnSlUOLaQ5PydnatZDlCHtIYFAGCjE6gBAMAGt72jNbt7uioau7unK9vbW1e5IqDeaA0LAMBGJ1ADAACyv7c7OzrKB2U7OlpzxZ7uNaoIqCdawwIAsNEJ1AAAgHS1teSai3alt6drQfvHQpLenq5ce/GudG5pqUV5QI1pDQsAwEa3qdYFAAAA9aGrrSUH9p6bwbGJHLj6V9O8ZWumj47kkzdcr80jbHCzrWH7+odOOVZrWAAAGpFADQAAmGd7e2tG+v58ztcfr2E1QL3Y39udQ0fGc3h0YskxWsMCANCoBGoAAADAKc22hr25byAH+4dSmrOvkJmVaVfs6dYaFgBY9wZHJ3LvwHDGJ6fT1tKcC3Z2WoGPQA0AAACojNawAEAjGxqfXPTNQ7f2DWR3T1f293anq82bhzYqgRoAAABQFa1hAYBGMzQ+metuf3DR9talJH39Qzl0ZDzXXrzLivwNqqnWBQAAAAAAANTSzX0DZe8VmySHRyfyhXsG1qgi6o1ADQAAAAAA2LAGRydysH+oorEH+4cyOFY+eKMxCdQAAAAAAIAN696B4Xn3TCunlOS+R4dXsxzqlEANAAAAAADYsMYnp6saP1bleBqDQA0AAAAAANiw2lqaqxrfXuV4GoNADQAAAAAA2LAu6O5MocKxhSTn7+xczXKoU5tqXQAAAAAAAECtbO9oze6ervT1D51y7O6ermxvb12DqhYaHJ3I3xwaSlNTITvaNuXlz9yep3duqUktG5FADQAAAAAA2ND293bn0JHxHB6dWHLMjo7WXLGnew2rmjE0Ppmb+wZysH8opTnb/6CQ7Hv2jlxz8bOzo6M2Id9GouUjAAAAAACwoXW1teSai3alt6drQfvHQpLenq5ce/GudG5pWdO6hsYnc93tD6bvpDAtSYql5PYfHc6vfOFgflomCGRlrMgKtampqfzJn/xJbr311vT39+fss8/Oa1/72rztbW9LS8upL64nnngiN9xwQ+6444789Kc/za5du/Irv/Irueyyy1aiPAAAAAAAgLK62lpyYO+5GRybyIGrfzXNW7Zm+uhIPnnD9TVr83hz30DZVXNJ8ujQ0fz+1/4uv3f589eoqo1pRQK1D37wg7nlllty4YUX5qKLLso999yTG264IQ888EBuuOGGdIBGBwAAIABJREFUso8dGxvLW97ylnz/+9/Pz//8z+dnfuZn8tWvfjW//uu/nsHBwbz5zW9eiRIBAAAAAABOaXt7a0b6/nzO1x+vSR2DoxM5WMF93ZLkjr87nB8PH3VPtVW07EDtnnvuyS233JJLL700119/fQqFQkqlUt7znvfktttuy9e//vW84hWvWPLxN954Y773ve/l/e9/f970pjclSd7+9rdn//79+YM/+IP8/M//fM4666zllgkAAAAAALBu3DswvKDN41KKpeQbDw7m9b1rf4+3jWLZ91C76aabkiTvfOc7UyjMdBYtFAp517velUKhkC9+8YtlH//5z38+O3bsyP79+09s27p1a6688sqMj4/nz/7sz5ZbIgAAAAAAwLoyPjld1fgnj02uUiUkKxCo3X333TnzzDPz3Oc+d972c845J+edd17uuuuuJR/7yCOP5LHHHsuFF16Y5ubmefte/OIXJ0nZxwMAAAAAADSitpbmUw+aY9vmllWqhGSZgdrExER+/OMf5xnPeMai+3fu3Jnh4eEMDg4uuv+RRx5JkkUff/bZZ2fz5s35+7//++WUCAAAAAAAsO5c0N2ZQoVjmwrJz+3avqr1bHSFUqlUaQvOBX7yk5/k5S9/eV72spfls5/97IL9v/Zrv5a/+Iu/yFe/+tWce+65C/Z/+ctfzrvf/e68+93vztve9rYF+/fu3ZtNmzblG9/4xumWmO8NDOX/u+Fbp/14AAAAAAAA1p8/e+fL8v/r6VqRYy1rhdrU1FSSpLW1ddH9s9uPHTt22o9f6rGVKhaX9XAAAAAAAAA2uGUFalu2bEmSTE4ufqO7iYmJJElbW9ui+zdv3jxv3GKPb29vX06Jaap0PSQAAAAAAAAsYtNyHrx169Y0NTVlZGRk0f1PPvlkkmTbtm2L7u/qmllmt9TjR0ZGctZZZy2nxDz3aVtz17t/blnH2CjOOGMmvHziibEaV7JQobmQv3t8NE+MLh6+wnJs3ToT7o+MLG9F7Ebx8xc+dd/Lv/juIzWsZD51VUdd1VFXYzDfsxG57tkoPvnX/5C+/qFTjuvt6cqBvQtvSbFW6vXfbnVVR13VUVd11FUddVWnXuuicoNjE/nR4HgKhULObmvJy555Zp7euaXWZdW12dxjJSwrUGttbU13d3f6+/sX3d/f358zzzwzZ5xxxqL7zzvvvBPjTvaTn/wkx44dyzOf+czllAgAAAA0sMHRiRysIExLkoP9Qxkcm8j29sVvPQEAUM+2t7fm0qd35pyutnQ1FzI97Z5Xa2lZLR+T5MILL8zjjz+ehx9+eN72xx57LP/wD/+Q3bt3L/nY7u7udHd357vf/W6KJ93s7M4770yS9Pb2LrdEAAAAoEHdOzCcUoVjS0nue3R4NcsBAKBBLTtQe/WrX50k+chHPnIiFCuVSvnwhz+cUqmUN7zhDWUff/nll+fHP/5x/uRP/uTEtpGRkXziE5/Ili1b8q//9b9ebokAAABAgxqfnK5q/FiV4wEAIFlmy8ck2bt3by677LJ85StfyRve8Ia8+MUvTl9fX+6+++5ceuml2bdv34mxH/3oR5MkV1111Yltb33rW/O///f/zu/+7u/mrrvuys/+7M/mq1/9ag4dOpTf/u3fzvbt25dbIgAAANCg2lqaqxrfXuV4AABIVmCFWpL8/u//fq6++uocOXIkf/zHf5zDhw/n6quvzh/8wR+kUCicGPexj30sH/vYx+Y9duvWrbnpppvyb/7Nv8ndd9+dz3/+8+ns7MyHP/zhvPnNb16J8gAAAIAGdUF3ZwqnHpYkKSQ5f2fnapYDAECDWvYKtSRpaWnJO97xjrzjHe8oO+6BBx5YdPuOHTvyn//zf16JUgAAAIANZHtHa3b3dKWvf+iUY3f3dGV7e+saVAUAQKNZkRVqAAAAALWyv7c7OzrKB2U7OlpzxZ7uNaoIAIBGI1ADAAAA1rWutpZcc9Gu9PZ0LWj/WEjS29OVay/elc4tLbUoDwCABrAiLR8BAFhbg6MT2bbnlWna3JHisdEMjk1oYQXAhtbV1pIDe8/N4NhEHjg8ltGJ6WwqlXL+zk7/RgIAsGwCNQCAdWRofDI39w3kYP9Qtl9y5Ynt7/vy/dnd05X9vd3pavPuewA2ru3trbnk+duSJCMjx2pcDQAAjUKgBgCwTgyNT+a62x/M4dGJBftKSfr6h3LoyLiWVgAAQMPTtaM6zhcsn0ANAGCduLlvYNEwba7DoxP5wj0DObD33DWqCgAAYO3o2lEd5wtWjkANAGAdGBydyMH+oYrGHuwf8m5DAABgRdTTyqb10LXD+YLGJVADAFgH7h0YTqnCsaUk9z06nH3P2bGaJQEAAA2sHlc21XPXDucLGl9TrQsAAODUxienqxo/VuV4AACAWbMrm/r6hxa8sW92ZdN1tz+Y4aOTa1bT6XTtWCvOF2wMAjUAgHWgraW5qvHtVY4HAACYVc3KprVyOl071orzBRuDQA0AYB24oLszhQrHFpKcv7NzNcsBAAAaVL2ubKrXrh3OF2wcAjUAgHVge0drdvd0VTR2d09XzW56DQAArG/1urKpXrt2OF+wcQjUAADWif293dnRUT4o29HRmiv2dK9RRQAAQKOp15VN9dq1w/mCjUOgBgCwTnS1teSai3alt6drwQujQpLenq5ce/GudG5pqUV5AABAA6jXlU312rXD+YKNY1OtCwAAoHJdbS05sPfcDI5N5L5HhzM2OZ32luacv7PTCyAAAGDZLujuzK19AxW1MVzrlU37e7tz6Mh4Do8ufR+yte7a4XzBxmGFGgDAOrS9vTX7nrMjlz3/nOx7zg5hGgAAsCLqeWVTPXbtcL5g47BCDQAAAACAE+p5ZdPcrh0PHB7L6MR0NpVKNe3asV7Oly4nsDwCNQAAAAAATphd2XRz30AO9g/Na2dYyMxKqyv2dNd0ZdP29tZc8vxtSZKRkWM1qyNZP+dr33N21Oz5oREI1AAAAAAAmMfKpuo4X9D4BGoAAAAAUIHB0Yls2/PKNG3uSPHYaAbHJvyinIZnZVN1nC9oXAI1AAAAAChjaHzyRCu37ZdceWL7+758f3b3dGV/b3e62mrXyg0AWH1NtS4AAAAAAOrV0Phkrrv9wfSddF+kJCkl6esfynW3P5jho5O1KA8AWCMCNQAAAABYws19Azk8OlF2zOHRiXzhnoE1qggAqAWBGgAAAAAsYnB0Igf7hyoae7B/KINj5YM3AGD9EqgBAAAAwCLuHRhe0OZxKaUk9z06vJrlAAA1tKnWBQBAoxocnci2Pa9M0+aOFI+NZnBsItvbW2tdFgAAUKHxyemqxo9VOR7m8hoSoL4J1ABghQ2NT+bmvoEc7B/K9kuuPLH9fV++P7t7urK/tztdbS01rBAAAKhEW0tzVePbqxwPideQAOuFlo8AsIKGxidz3e0Ppq9/aEFrmFKSvv6hXHf7gxk+OlmL8gAAgCpc0N2ZQoVjC0nO39m5muXQgLyGBFg/BGoAsIJu7hvI4dHyNyI/PDqRL9wzsEYVAQDA+jPb+q7rJW/Itj2vzOBY+Z+xV8v2jtbs7umqaOzuni7t+aia15AA64eWjwCwQgZHJ3Kwf6iisQf7h/TDBwCAk9Rj67v9vd05dGS8bOixo6M1V+zpXsOqaAReQwKsL1aoAcAKuXdgeEGLjqWUktz36PBqlgMAAOtKvba+62pryTUX7UpvT9eC9o+FJL09Xbn24l3p3OIeV1THa0iA9cUKNQBYIeOT01WNH6tyPAAANLJqWt8d2HvuGlU1o6utJQf2npvBsYnc9+hwxian097SnPN3dloxxGnzGhJgfRGoAcAKaWtprmp8e5XjAQBYv2bvCda0uSPFY6Nat51kvbS+297emn3P2bHmz0tj8hoSYH0RqAHACrmguzO39g1U1LKjkOT8nZ2rXRIAADVWj/cEq0en0/pOsMV65zUkwPriHmoAsEK2d7Rmd09XRWN393R5RzIAQIOr13uC1SOt79iIvIYEWF8EagBUbLZNTddL3pBte16ZwbHy9zfYiPb3dmdHR/kXOTs6WnPFnu41qggAgFqp5p5gG53Wd2xUXkMCrB9aPgJwStrUVK6rrSXXXLTrxPma+07kQmbeVXjFnu50bnG+1gv3OwEATsd6uSdYvdD6jo3Ka0iA9UOgBkBZs21qFntn7WybmkNHxnPtxbv8gH9cV1tLDuw9N4NjE7nv0eGMTU6nvaU55+/s3NC/JDmVeguuBMkAwHK4J1h1Zlvf9VUQQmp9R6PxGhJgfRCoAVBWNW1qDuw9d42qWh+2t7du6F+KVKoegytBMgCwXO4JVr39vd05dGS87OsPre9oZF5DAtQ391ADYEmn06YGqjEbXPWd1NokeSq4uu72BzN8dHJN63K/EwBgudwTrHqzre96e7pSOGlfIUlvT5c3NAEANWOFGgBL0qaG1VaPKyDd7wQAWAnuCXZ6tL4DAOqVQA2AJWlTw2qq1+BKkAwArAT3BFsere8AgHojUANgSdrUsJrqNbgSJAMAK8U9wVgrg6MT2bbnlWna3JHisVFdFABgFQjUAFiSNjWspnoNrgTJAMBKmb0n2M19Azl40j1jC5lZmXbFnm73BOO0DY1Pnri+tl9y5Ynt7/vy/dnd05X9vd3panN9AcBKEKgBsCRtalhN9RpcCZIBgJXknmCslqHxyVx3+4OLroAsJenrH8qhI+O59uJdQlsAWAFNtS4AgPq2v7c7OzrKv9DXpobTcUF3ZwoVjl3L4Go2SK6EIBkAqNTsPcEue/452fecHX6GYNlu7hso2040SQ6PTuQL9wysUUUA0NgEagCUNdumprena0H4UUjS29PlHY+clnoOrgTJAADUs8HRiRysoJNIkhzsH8rgWPngDQA4NS0fATglbWpYLft7u3PoyHjZd9bWIrhyvxMAAOrZvQPDFbUoT2baP9736HD2PWfHapYEAA1PoAZAxWbb1MBKqefgSpAMAEC9Gp+crmr8WJXjAYCFBGoAQE3Ve3AlSAYAoN60tTRXNb69yvEAwEICNQCgLgiuAACgMhd0d+bWvoGK2j4Wkpy/s3O1SwKAhtdU6wIAAAAAgMpt72jN7p6uisbu7umqi84PALDeCdQAAAAAYJ3Z39udHR3lg7IdHa25Yk/3GlUEAI1NoAYAAAAA60xXW0uuuWhXenu6UjhpXyFJb09Xrr14Vzq3tNSiPABoOO6hBgAAAADrUFdbSw7sPTeDYxN54PBYRiems6lUyvk7O7V5BIAVJlADAAAAgHVse3trLnn+tiTJyMixGlcDAI1JoAbAujc4OpFte16Zps0dKR4bzeDYhHdjluF8AQAAAEB1BGoArFtD45O5uW8gB/uHsv2SK09sf9+X78/unq7s7+1OV5v7BcxyvgAAAADg9AjUAFiXhsYnc93tD+bw6MSCfaUkff1DOXRk3E24j3O+AABYjO4FAACVEaixbhSStDQ3ZXK6WOtSgDpwc9/AouHQXIdHJ/KFewZyYO+5a1RV/XK+AACYS/cCAIDqNNW6AKhEc6GQczq3ZEfn5pzR0Zq21uY0FQq1LguokcHRiRzsH6po7MH+oQyOlQ+SGp3zBQDAXLPdC/r6h1I6ad9s94Lrbn8ww0cna1EeAEBdEqixLkxNFbOttTnPOKMtzzyrIz/T1Zaztm3OtraWtG5yGcNGc+/A8IIX/kspJbnv0eHVLKfuOV8AAMxVTfcCWI7ZlqJdL3lDtu15pTfvAbCuafnIulEsllIsltJcSHZ0tObsra0ZOTaV4aPTGTk2mYmpYo5OTme6WOmvjYH1anxyuqrxY1WObzTOFwAAs06ne4F7qlEtLUUBaEQCNdadUimZPn4ftfaW5mzdvCnTpdYMj09l+OhkxieLOTY5nWNT0ynJ1qAhtbU0VzW+vcrxjcb5AgBg1ul0L9j3nB2rWRINZral6GKrIGdbih46Mp5rL96Vzi1CNQDWD73yWNeKxVKmpoopTZfStWVTzt3envPOas/Tu7Zk+9bN2bqlJS3NLnNoNBd0d6bSuygWkpy/s3M1y6l7zhcAALN0L2C1aSkKQKOSNNAwZsO1lkIhT9u6Oc86qyM/u70tT+vckjM6WtPW2pymQqW/Ugbq2faO1uzu6apo7O6erg3fosb5AgBglu4FrKbTaSkKAOuFQI2GUyqVMj1dTHG6mK0tzek5Y0uedVZHus9oy1nbNqezrSWbNzVVvFoDqE/7e7uzo6N88LOjozVX7Oleo4rqm/MFAECiewGr63RaigLAeiFQo6HNrlprSilntbfkWWe15xnb23NOV1vO2Lo5HZs3ZVOzaA3Wo662llxz0a709nQt+IVAIUlvT5ee/HM4XwAAJLoXsLq0FAWgkW2qdQGwFkqlZHq6lKSULc1Nad+2Oedsa83w0ekMHZ3M+MR0jk1N59jkdEqVvpUKqLmutpYc2HtuBscmct+jwxmbnE57S3PO39nphf8i5p6vBw6PZXRiOptKJecLAGCD2d/bnUNHxsve50r3Ak6HlqIANDKBGhvOTEvImdSsc3NzzmjblGNTxQyNT+bJY9M5ejxYm5wq1rhSoFLb21uz7zk7al3GurG9vTWXPH9bkmRk5FiNqwEAYK3Ndi+4uW8gB/uH5rXoK2RmZdoVe7p1L6BqF3R35ta+gYraPmopCsB6I1BjQysWSykWS9lUSM7eujlnb0tGjk1laHwqoxNTmZgq5tjkdKaLlq0BAADQOHR7YDXMthTt6x865VgtRQFYbwRqkNmWkDMr0jpamrNt86ZMFUsZOjqV4fHJ46vWipmYnK745roAAABQ73R7YKVpKQpAo2qqdQFQb4rFUqamikmxlO1tm/LMszpy7vb2nNO1JWdu3ZyOLZvS0uyvDgAAAMDJZluK9vZ0pXDSvkKS3p6uXHvxLi1FAVh3rFCDMmbutTadzU1NefrWzSlt3Zzho1MZPjaZ8YmZe61NTBW1hAQAAAA4rt5big6OTmTbnlemaXNHisdGMzg2URd1AVDfBGpQgVKpdDxcSzo3N+eMtk05NlXK8NHJjE1O59hUMcViKdPFUqaLxeMfS4I2AAAAYMOqt5aiQ+OTubl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2d8a2cf8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 535,
       "width": 874
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.tsa.api as smt\n",
    "\n",
    "acf = smt.graphics.plot_acf(lin_reg.resid, lags=40 , alpha=0.05)\n",
    "acf.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-31T23:56:45.798834Z",
     "start_time": "2019-05-31T23:56:45.795407Z"
    }
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "\n",
    "library(ggplot2)\n",
    "library(lmtest)\n",
    "\n",
    "acf(lin_reg$residuals)  \n",
    "dwtest(lin_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Potential solutions:\n",
    "* in case of minor positive autocorrelation, there might be some room for fine-tuning the model, for example, adding lags of the dependent/independent variables \n",
    "* some seasonal components might not be captured by the model, account for them using dummy variables or seasonally adjust the variables\n",
    "* if `DW < 1` it might indicate a possible problem in model specification, consider stationarizing time-series variables by differencing, logging, and/or deflating (in case of monetary values)\n",
    "* in case of significant negative correlation, some of the variables might have been overdifferenced\n",
    "* use Generalized Least Squares\n",
    "* include a linear (trend) term in case of a consistent increasing/decreasing pattern in the residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other assumptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-02T22:40:27.719645Z",
     "start_time": "2019-06-02T22:40:27.715993Z"
    }
   },
   "source": [
    "Below I present some of the other commonly verified assumptions of linear regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The features and residuals are uncorrelated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate this assumption I check the Pearson correlation coefficient between each feature and the residuals. Then report the p-value for testing lack of correlation between the two considered series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:21:11.592884Z",
     "start_time": "2019-06-03T19:21:11.582404Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: CRIM --- correlation: 0.0000, p-value: 1.0000\n",
      "Variable: ZN --- correlation: -0.0000, p-value: 1.0000\n",
      "Variable: INDUS --- correlation: 0.0000, p-value: 1.0000\n",
      "Variable: NOX --- correlation: 0.0000, p-value: 1.0000\n",
      "Variable: RM --- correlation: -0.0000, p-value: 1.0000\n",
      "Variable: AGE --- correlation: 0.0000, p-value: 1.0000\n",
      "Variable: DIS --- correlation: -0.0000, p-value: 1.0000\n",
      "Variable: RAD --- correlation: 0.0000, p-value: 1.0000\n",
      "Variable: TAX --- correlation: 0.0000, p-value: 1.0000\n",
      "Variable: PTRATIO --- correlation: 0.0000, p-value: 1.0000\n",
      "Variable: B --- correlation: -0.0000, p-value: 1.0000\n",
      "Variable: LSTAT --- correlation: 0.0000, p-value: 1.0000\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats.stats import pearsonr\n",
    "\n",
    "for column in X.columns:\n",
    "    corr_test = pearsonr(X[column], lin_reg.resid)\n",
    "    print(f'Variable: {column} --- correlation: {corr_test[0]:.4f}, p-value: {corr_test[1]:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:21:14.347430Z",
     "start_time": "2019-06-03T19:21:14.273252Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Variable: CRIM --- correlation: -1.23790541661334e-17 , p-value: 1\"\n",
      "[1] \"Variable: ZN --- correlation: -1.97025792143074e-17 , p-value: 1\"\n",
      "[1] \"Variable: INDUS --- correlation: 1.86969111454043e-16 , p-value: 0.999999999999997\"\n",
      "[1] \"Variable: NOX --- correlation: 7.78555411954571e-18 , p-value: 1\"\n",
      "[1] \"Variable: RM --- correlation: -1.87370697712339e-17 , p-value: 1\"\n",
      "[1] \"Variable: AGE --- correlation: -2.12484228685413e-16 , p-value: 0.999999999999996\"\n",
      "[1] \"Variable: DIS --- correlation: -2.44313238639036e-17 , p-value: 1\"\n",
      "[1] \"Variable: RAD --- correlation: 3.00418785293366e-16 , p-value: 0.999999999999995\"\n",
      "[1] \"Variable: TAX --- correlation: -1.83699416677024e-16 , p-value: 0.999999999999997\"\n",
      "[1] \"Variable: PTRATIO --- correlation: 2.64955201707963e-15 , p-value: 0.999999999999952\"\n",
      "[1] \"Variable: B --- correlation: -3.77372138576403e-16 , p-value: 0.999999999999993\"\n",
      "[1] \"Variable: LSTAT --- correlation: -1.07867254452892e-16 , p-value: 0.999999999999998\"\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "\n",
    "for (i in 1:(dim(X)[2])){\n",
    "    cor_test <- cor.test(X[, i], lin_reg$residuals)  # \n",
    "    print(paste('Variable:', colnames(X)[i], \n",
    "                '--- correlation:', as.character(cor_test$estimate), \n",
    "                ', p-value:', as.character(cor_test$p.value), sep = \" \", collapse = NULL))\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I cannot reject the null hypothesis (lack of correlation) for any pair."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The number of observations must be greater than number of features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one is pretty straightforward. We can check the shape of out data by using `shape` method in Python or `dim` function in R. Also, a rule of thumb says that we should have more than 30 observations in the dataset. This is taken from the Central Limit Theorem, which states that adding IID random variable results in a normalized distribution when the sample size is greater than 30, even when the random variables are not Gaussian themselves."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There must be some variability in features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This assumption states that there must be some variance in the features, as a feature that has a constant value for all or majority of observations might not be a good predictor. We can check this assumption by simply checking the variance of all features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:21:19.824976Z",
     "start_time": "2019-06-03T19:21:19.812214Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM          73.840360\n",
       "ZN           542.861840\n",
       "INDUS         46.971430\n",
       "NOX            0.013401\n",
       "RM             0.492695\n",
       "AGE          790.792473\n",
       "DIS            4.425252\n",
       "RAD           75.666531\n",
       "TAX        28348.623600\n",
       "PTRATIO        4.677726\n",
       "B           8318.280421\n",
       "LSTAT         50.893979\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.apply(np.var, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `caret` package in R there is a function called `nearZeroVar` for identifying features with zero or near-zero variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-06-03T19:21:20.863Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Loading required package: lattice\n",
      "\n",
      "R[write to console]: Loading required package: ggplot2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "library(caret)\n",
    "\n",
    "apply(X, 2, var)\n",
    "nearZeroVar(X, saveMetrics= TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normality of residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When this assumption is violated, it causes problems with calculating confidence intervals and various significance tests for coefficients. When the error distribution significantly departs from Gaussian, confidence intervals may be too wide or too narrow.\n",
    "\n",
    "Some of the potential reasons causing non-normal residuals:\n",
    "* presence of a few large outliers in data \n",
    "* there might be some other problems (violations) with the model assumptions\n",
    "* another, better model specification might be better suited for this problem\n",
    "\n",
    "Technically, we can omit this assumption if we assume instead that the model equation is correct and our goal is to estimate the coefficients and generate predictions (in the sense of minimizing mean squared error).\n",
    "\n",
    "However, normally we are interested in making valid inferences from the model or estimating the probability that a given prediction error will exceed some threshold in a particular direction. To do so, the assumption about normality of residuals must be satisfied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate this assumption we can look at:\n",
    "* QQ plots of the residuals (a detailed description can be found [here](https://towardsdatascience.com/explaining-probability-plots-9e5c5d304703)). For example a bow-shaped pattern of deviations from the diagonal implies that the residuals have excessive skewness (i.e., the distribution is not symmetrical, with too many large residuals in one direction). S-shaped pattern of deviations implies excessive kurtosis of the residuals - there are either too many or two few large errors in both directions.\n",
    "* use statistical tests such as the Kolmogorov-Smirnov test, the Shapiro-Wilk test, the Jarque-Bera test and the Anderson-Darling test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-03T19:21:59.948120Z",
     "start_time": "2019-06-03T19:21:59.585009Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jarque-Bera test ---- statistic: 898.3521, p-value: 0.0\n",
      "Shapiro-Wilk test ---- statistic: 0.8953, p-value: 0.0000\n",
      "Kolmogorov-Smirnov test ---- statistic: 0.3283, p-value: 0.0000\n",
      "Anderson-Darling test ---- statistic: 10.9109, 5% critical value: 0.7810\n",
      "If the returned AD statistic is larger than the critical value, then for the 5% significance level, the null hypothesis that the data come from the Normal distribution should be rejected. \n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2a4e5b70>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 548,
       "width": 890
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "def normality_of_residuals_test(model):\n",
    "    '''\n",
    "    Function for drawing the normal QQ-plot of the residuals and running 4 statistical tests to \n",
    "    investigate the normality of residuals.\n",
    "    \n",
    "    Arg:\n",
    "    * model - fitted OLS models from statsmodels\n",
    "    '''\n",
    "    sm.ProbPlot(model.resid).qqplot(line='s');\n",
    "    plt.title('Q-Q plot');\n",
    "\n",
    "    jb = stats.jarque_bera(model.resid)\n",
    "    sw = stats.shapiro(model.resid)\n",
    "    ad = stats.anderson(model.resid, dist='norm')\n",
    "    ks = stats.kstest(model.resid, 'norm')\n",
    "    \n",
    "    print(f'Jarque-Bera test ---- statistic: {jb[0]:.4f}, p-value: {jb[1]}')\n",
    "    print(f'Shapiro-Wilk test ---- statistic: {sw[0]:.4f}, p-value: {sw[1]:.4f}')\n",
    "    print(f'Kolmogorov-Smirnov test ---- statistic: {ks.statistic:.4f}, p-value: {ks.pvalue:.4f}')\n",
    "    print(f'Anderson-Darling test ---- statistic: {ad.statistic:.4f}, 5% critical value: {ad.critical_values[2]:.4f}')\n",
    "    print('If the returned AD statistic is larger than the critical value, then for the 5% significance level, the null hypothesis that the data come from the Normal distribution should be rejected. ')\n",
    "    \n",
    "normality_of_residuals_test(lin_reg)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "%%R\n",
    "library(tseries)\n",
    "library(olsrr)\n",
    "\n",
    "qqnorm(lin_reg$residuals)\n",
    "\n",
    "# or \n",
    "\n",
    "df_resid <- data.frame(resid = lin_reg$residuals)\n",
    "p <- ggplot(df_resid, aes(sample = resid))\n",
    "p + stat_qq() + stat_qq_line()\n",
    "\n",
    "jarque.bera.test(lin_reg$residuals)\n",
    "ks.test(as.numeric(lin_reg$residuals), \"pnorm\")\n",
    "\n",
    "ols_test_normality(lin_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the results above we can infer that the residuals do not follow Gaussian distribution - from the shape of the QQ plot, as well as rejecting the null hypothesis in all statistical tests. The reason why Kolmogorov-Smirnov from `ols_test_normality` shows different results is that it does not run the `two-sided` version of the test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Potential solutions:\n",
    "* nonlinear transformation of target variable or features\n",
    "* remove/treat potential outliers\n",
    "* it can happen that there are two or more subsets of the data having different statistical properties, in which case separate models might be considered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not really an assumption, however the existence of outliers in our data can lead to violations of some of the above-mentioned assumptions. That is why we should investigate the data and verify if some extreme observations are valid and important for our research or merely some errors which we can remove. \n",
    "\n",
    "I will not dive deep into outlier detection methods as there are already many articles about them. A few potential approaches:\n",
    "* Z-score\n",
    "* box plot\n",
    "* Leverage - measure of how far away the feature values of a point are from the values of the different observations. High-leverage point is a point at extreme values of the variables, where lack of nearby observations makes the fitted regression model pass close to that particular point.\n",
    "* Cook’s distance - measure of how deleting an observation impacts the regression model. It makes sense to investigate points with high Cook’s distances.\n",
    "* Isolation Forest - for more details see [this article](https://towardsdatascience.com/outlier-detection-with-isolation-forest-3d190448d45e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-02T22:48:27.449066Z",
     "start_time": "2019-06-02T22:48:27.446068Z"
    }
   },
   "source": [
    "http://people.duke.edu/~rnau/testing.htm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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